classifications of credit cardholder behavior by using multiple criteria non-linear programming

Authors:
Jing He;Yong Shi;Weixuan Xu
Affiliations:
Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China;Graduate School of Chinese Academy of Sciences, Beijing, China;Institute of Policy and Management, Chinese Academy of Sciences, Beijing, China
Venue:
CASDMKM'04 Proceedings of the 2004 Chinese academy of sciences conference on Data Mining and Knowledge Management
Year:
2004

Citing 1
Cited 5

Induction of Decision Trees

Machine Learning

Credit Risk Evaluation Using Support Vector Machine with Mixture of Kernel

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part II
Multiple criteria programming models for VIP E-Mail behavior analysis

Web Intelligence and Agent Systems
Evolution strategy based adaptive Lq penalty support vector machines with Gauss kernel for credit risk analysis

Applied Soft Computing
Personal bankruptcy prediction by mining credit card data

Expert Systems with Applications: An International Journal
Decision Rule Extraction for Regularized Multiple Criteria Linear Programming Model

International Journal of Data Warehousing and Mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

Behavior analysis of credit cardholders is one of the main research topics in credit card portfolio management. Usually, the cardholder’s behavior, especially bankruptcy, is measured by a score of aggregate attributes that describe cardholder’s spending history. In the real-life practice, statistics and neural networks are the major players to calculate such a score system for prediction. Recently, various multiple criteria linear programming based classification methods have been explored for analyzing credit cardholders’ behavior. This paper proposes a multiple criteria non-linear programming (MCNP) approach to discovering the bankruptcy patterns of credit cardholders. A real-life credit database from a major US bank is used for empirical study on MCNP classification. Finally, the comparison of MCNP and other known classification methods is conducted to verify the validation of MCNP method.